




'''simple docstring'''



UpperCamelCase_    	=				{
    "A": ["B", "C", "E"],
    "B": ["A", "D", "E"],
    "C": ["A", "F", "G"],
    "D": ["B"],
    "E": ["A", "B", "D"],
    "F": ["C"],
    "G": ["C"],
}




def   lowercase__(			__UpperCamelCase:					dict      ,__UpperCamelCase:					Dict      ,__UpperCamelCase:					List[Any]    ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      int    					=						set()
      # keep track of all the paths to be checked
      SCREAMING_SNAKE_CASE   :      Any    					=						[[start]]

      # return path if start is goal
      if start == goal:
            return [start]

      # keeps looping until all possible paths have been checked
      while queue:
            # pop the first path from the queue
            SCREAMING_SNAKE_CASE   :      List[Any]    					=						queue.pop(0    )
            # get the last node from the path
            SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						path[-1]
            if node not in explored:
                  SCREAMING_SNAKE_CASE   :      int    					=						graph[node]
                  # go through all neighbour nodes, construct a new path and
                  # push it into the queue
                  for neighbour in neighbours:
                        SCREAMING_SNAKE_CASE   :      Tuple    					=						list(__UpperCamelCase    )
                        new_path.append(__UpperCamelCase    )
                        queue.append(__UpperCamelCase    )
                        # return path if neighbour is goal
                        if neighbour == goal:
                              return new_path

            # mark node as explored
                  explored.add(__UpperCamelCase    )

    # in case there's no path between the 2 nodes
      return []




def   lowercase__(			__UpperCamelCase:					dict      ,__UpperCamelCase:					Dict      ,__UpperCamelCase:					List[str]    ):

      """simple docstring"""






      if not graph or start not in graph or target not in graph:
            return -1
      if start == target:
            return 0
      SCREAMING_SNAKE_CASE   :      str    					=						[start]
      SCREAMING_SNAKE_CASE   :      List[Any]    					=						set(__UpperCamelCase    )
      # Keep tab on distances from `start` node.
      SCREAMING_SNAKE_CASE   :      int    					=						{start: 0, target: -1}
      while queue:
            SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						queue.pop(0    )
            if node == target:
                  SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						(
                      dist[node] if dist[target] == -1 else min(dist[target]      ,dist[node]    )
                  )
            for adjacent in graph[node]:
                  if adjacent not in visited:
                        visited.add(__UpperCamelCase    )
                        queue.append(__UpperCamelCase    )
                        SCREAMING_SNAKE_CASE   :      List[Any]    					=						dist[node] + 1
      return dist[target]


if __name__ == "__main__":
      print(bfs_shortest_path(demo_graph, "G", "D"))  # returns ['G', 'C', 'A', 'B', 'D']
      print(bfs_shortest_path_distance(demo_graph, "G", "D"))  # returns 4








'''simple docstring'''







from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL

from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
from .timesteps import (
    fastaa_timesteps,
    smartaa_timesteps,
    smartaa_timesteps,
    smartaaa_timesteps,
    smartaaa_timesteps,
    superaa_timesteps,
    superaa_timesteps,
    superaaa_timesteps,
)

@dataclass
class 	_snake_case(			UpperCAmelCase      ):
 __snake_case:							Union[List[PIL.Image.Image], np.ndarray]
 __snake_case:							Optional[List[bool]]
 __snake_case:							Optional[List[bool]]


try:
    if not (is_transformers_available() and is_torch_available()):
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
    from .pipeline_if import IFPipeline
    from .pipeline_if_imgaimg import IFImgaImgPipeline
    from .pipeline_if_imgaimg_superresolution import IFImgaImgSuperResolutionPipeline
    from .pipeline_if_inpainting import IFInpaintingPipeline
    from .pipeline_if_inpainting_superresolution import IFInpaintingSuperResolutionPipeline
    from .pipeline_if_superresolution import IFSuperResolutionPipeline
    from .safety_checker import IFSafetyChecker
    from .watermark import IFWatermarker


